Leveraging edge-aware graph neural networks to predict node load in backbone networks
Wagner Almeida, Fabio Ramos, José Augusto Nacif, Ricardo Ferreira, Alex Borges · Journal of Complex Networks · 2025
Abstract Graph Neural Networks (GNNs) are effective machine learning (ML) techniques to study network data, particularly for large and complex systems. Many state-of-the-art models mainly consider properties of nodes and do not take into account that the structural and functional properties of edges can efficiently affect the behavior of a system. In this work, we develop and evaluate AttEAGNN, an attention-based, edge-aware GNN architecture designed for incorporating edge information when predicting network dynamics. We apply our model to the practical problem of predicting node load in two real-world backbone networks. By creating a hybrid architecture that processes nodes and edges in parallel and encodes both explicit link attributes and implicit topological features (e.g. edge centrality), our model generates a more robust representation of link-based structure. Our results demonstrate that this edge-aware approach consistently achieves higher performance on this task when compared to state-of-the-art GNNs that do not integrate rich edge features.